---
language:
- en
- fr
- es
- de
- it
- pt
- nl
license: apache-2.0
tags:
- tabular
- multi-modal
- nutriscore
- nutrition
pipeline_tag: tabular-classification
datasets:
- hsilvosa/open-food-facts
metrics:
- accuracy
- mae
- r2
---

# Nutri-Score Multi-Modal Tabular Predictor

This model estimates Nutri-Score grades (A, B, C, D, E) and continuous numerical scores when nutritional values are partially or fully missing.

## Dataset

Trained on [hsilvosa/open-food-facts](https://huggingface.co/datasets/hsilvosa/open-food-facts).

## Model Description

Nutri-Score is a front-of-pack nutritional rating system ranging from A (highest nutritional quality) to E (lowest). When product labels have missing macronutrient values (e.g., missing fiber or sodium content), standard rule-based calculators fail.

This multi-modal ensemble combines:
1. Available continuous macronutrients (energy, fat, saturated fat, sugars, proteins, fiber, sodium, salt).
2. Explicit missingness indicator masks for each nutrient.
3. Derived nutrient ratios (sugars-to-carbs, sat-to-total-fat, energy density).
4. Embedded product name, category, and ingredient list text representations.

## Performance Metrics

| Metric | Score |
|---|---|
| **Grade Top-1 Accuracy** | **83.33%** |
| **Grade Top-2 Accuracy** | **93.40%** |
| **Numeric Score R²** | **0.9051** (90.5% Variance Explained) |
| **Mean Absolute Error (MAE)** | **1.95 points** |
| **Median Absolute Error** | **0.95 points** |
| **Weighted F1-Score** | **0.8353** |

## Python Usage Example

```python
from src.models.nutriscore_predictor import NutriScorePredictor
import pandas as pd

predictor = NutriScorePredictor.from_pretrained("your-username/nutriscore-predictor")

sample_product = pd.DataFrame([{
    "product_name": "Organic Oat Drink",
    "categories": "Plant-based beverages",
    "ingredients_text": "Water, oats (12%), sunflower oil, sea salt.",
    "energy-kcal_100g": 45.0,
    "fat_100g": 1.5,
    "sugars_100g": 4.0,
    "proteins_100g": 0.8,
}])

prediction = predictor.predict(sample_product)[0]
print(f"Predicted Grade: {prediction['predicted_grade']}")
print(f"Predicted Score: {prediction['predicted_score']:.2f}")
```